Metabolic Basis of Bacterial Community Function in the Cystic Fibrosis Airway

囊性纤维化气道细菌群落功能的代谢基础

• 批准号: 10416061
• 负责人: George A. O'Toole
• 金额: $ 45.05万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-02 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10416061
• 关键词: Acute; Acute Disease; Antibiotics; Antimicrobial Resistance; Bacteria; Big Data; Bioinformatics; Cell Culture Techniques; Chronic; Chronic Disease; Clinical; Clinical Data; Communities; Complex; Computer Models; Consumption; Culture-independent methods; Cystic Fibrosis; Data; Data Set; Development; Disease; Disease Outcome; Dose; Energy-Generating Resources; Event; Exhibits; Experimental Models; Exposure to; Feeding Patterns; Genetic Diseases; Genetic study; In Vitro; Individual; Infection; Lung; Lung infections; Machine Learning; Measures; Metabolic; Metadata; Microbe; Microbial Physiology; Minimum Inhibitory Concentration measurement; Modeling; Morbidity - disease rate; Mucous body substance; Mutation; Nature; Organism; Outcome; Outcome Measure; Output; Pathway interactions; Patients; Pharmaceutical Preparations; Phenotype; Prognosis; Pseudomonas aeruginosa; Pseudomonas aeruginosa infection; Pulmonary Cystic Fibrosis; Pulmonary Function Test/Forced Expiratory Volume 1; Regimen; Research; Resistance; Role; Sampling; Sputum; Staphylococcus aureus; Streptococcus; Structure; Testing; Virulence; Work; antibiotic tolerance; antimicrobial; antimicrobial drug; bacterial community; base; bioinformatics tool; cystic fibrosis airway; cystic fibrosis infection; cystic fibrosis patients; experimental study; feeding; improved; in silico; in vitro Model; in vivo; insight; member; metabolomics; microbial; microbial community; mortality; novel; novel therapeutics; pathogenic bacteria; polymicrobial biofilm; polymicrobial disease; pulmonary function; pulmonary function decline; success; tool; translational impact

Abstract. Cystic fibrosis (CF) is a fatal genetic disease characterized by overproduction of mucus in the lungs followed by chronic lung infections. Conventional wisdom has been that most CF lung infections involve a single dominant organism, most commonly the pathogenic bacterium Pseudomonas aeruginosa. Advances in culture-independent techniques have revealed that CF lung infections are rarely mono-microbial and instead usually involve complex microbial communities, yet the interspecies interactions that drive these communities are poorly understood. Furthermore, numerous studies have demonstrated that polymicrobial infections are more difficult than mono-microbial infections to eradicate with antibiotics, leading to the concept of recalcitrant communities. The mechanisms underlying recalcitrance are thought to involve synergistic interactions between community members, but very little data are available to understand this phenomenon. Combined with the realization that many CF patients respond poorly to available antibiotic regimens compels a more detailed understanding of interspecies interactions and their impacts on antibiotic recalcitrance to improve the treatment of CF infections, as well as other polymicrobial diseases. Here, we combine big-data bioinformatics, in silico computational modeling and in vitro culture experiments to gain insights into the metabolic interactions that drive CF disease outcomes and antibiotic recalcitrance. The research will leverage an available data set of hundreds of CF patient samples that provide both bacterial composition data and clinical metadata, including measures of lung function. These samples will be clustered according to their measured compositions and metabolic capabilities predicted through computational metabolic modeling to test the hypothesis that the vast complexity of these many bacterial communities can be collapsed into a small number of model communities that capture most of the observed metabolic variability. These computational predictions will be tested by developing in vitro cell culture models that recapitulate the most important metabolic features of the in vivo polymicrobial communities (Aim 1). By applying bioinformatics and modeling to the same clinical data, we will test the hypothesis that community metabolic features drive disease outcomes and the virulence potential of these communities (Aim 2). Finally, we will interrogate the clinical data and in vitro communities to test the hypothesis that community metabolic features drive antibiotic recalcitrance and differentiate community responsiveness to antibiotics according to these metabolic features (Aim 3). Our research will yield novel insights into how complex polymicrobial communities are compositionally structured, interact metabolically, contribute to disease and respond to antibiotics. Moreover, the research will validate in vitro models that offer the potential for development of novel antimicrobial strategies to better treat chronic, polymicrobial infections in CF and other diseases. Our transdisciplinary team offers the necessary expertise in bioinformatics, computational modeling, microbial physiology and CF polymicrobial infections to tackle this complex problem.

抽象的。囊性纤维化（CF）是一种致命的遗传性疾病，其特征是肺内粘液分泌过多 然后是慢性肺部感染传统观点认为，大多数CF肺部感染涉及 单一优势菌，最常见的致病菌铜绿假单胞菌。进展 非培养技术已经揭示CF肺部感染很少是单一微生物的， 通常涉及复杂的微生物群落，但驱动这些群落的物种间相互作用 我们对此知之甚少。此外，许多研究表明，多种微生物感染是 比单一微生物感染更难用抗生素根除，导致了抗真菌的概念。 社区.潜在的抗肿瘤机制被认为涉及以下因素之间的协同作用： 社区成员，但很少有数据可以了解这种现象。结合 认识到许多CF患者对可用的抗生素治疗方案反应不佳， 了解种间相互作用及其对抗生素耐药性的影响，以改善治疗 CF感染，以及其他多种微生物疾病。在这里，我们将联合收割机大数据生物信息学， 计算建模和体外培养实验，以深入了解代谢相互作用， 推动CF疾病结局和抗生素耐药性。这项研究将利用现有的数据集， 提供细菌组成数据和临床元数据的数百份CF患者样本，包括 测量肺功能。这些样品将根据其测量的组成进行聚类， 通过计算代谢模型预测的代谢能力，以测试这一假设，即 这些许多细菌群落的复杂性可以分解成少量的模型群落 捕捉到了大部分观察到的代谢变异。这些计算预测将由 开发体外细胞培养模型，重现体内最重要的代谢特征， 多微生物群落（Aim 1）。通过将生物信息学和建模应用于相同的临床数据， 测试假设，社区代谢特征驱动疾病的结果和毒力的潜力， 这些社区（目标2）。最后，我们将询问临床数据和体外社区，以测试 假设群落代谢特征驱动抗生素顽固性并区分群落 根据这些代谢特征对抗生素的反应性（目的3）。我们的研究将产生新的 深入了解复杂的多微生物群落是如何组成结构的，如何在代谢上相互作用， 导致疾病并对抗生素产生反应。此外，该研究将验证体外模型， 开发新的抗菌策略以更好地治疗慢性多种微生物感染的潜力， CF和其他疾病。我们的跨学科团队提供生物信息学方面的必要专业知识， 计算建模，微生物生理学和CF多微生物感染来解决这个复杂的问题。